eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing
eRevise: Using Natural Language Processing to Provide Formative Feedback on Text Evidence Usage in Student Writing
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eRevise：使用自然语言处理为学生写作中文本证据的使用提供形成性反馈
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Rafael Quintana
中科院分区：
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作者：
Haoran Zhang;Ahmed Magooda;D. Litman;R. Correnti;E. Wang;L. Matsumura;Emily Howe;Rafael Quintana
写一篇好文章通常需要学生在收到反馈后修改最初的论文草稿。我们提出了eRevise，这是一个基于网络的写作和修改环境，它使用为基于规则的作文评分生成的自然语言处理功能来触发关于学生在回应文本写作中使用证据的形成性反馈信息。通过帮助学生理解在写作中使用文本证据的标准，eRevise使学生能够更好地修改他们的论文草稿。在5年级和6年级的7个教室试行eRevise后，在学生收到形成性反馈并进行论文修改后，书面文本证据的使用质量有所提高。
Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubricbased essay scoring to trigger formative feedback messages regarding students’ use of evidence in response-to-text writing. By helping students understand the criteria for using text evidence during writing, eRevise empowers students to better revise their paper drafts. In a pilot deployment of eRevise in 7 classrooms spanning grades 5 and 6, the quality of text evidence usage in writing improved after students received formative feedback then engaged in paper revision.